A Bayes nonparametric framework for software-reliability analysis

This paper presents a Bayes nonparametric approach for tracking and predicting software reliability. We use the common assumptions on the software operational environment to get a stochastic model where the successive times between software failures are exponentially distributed; their failure rates have Markov priors. Under these general assumptions we give Bayes estimates of the parameters that assess and predict the software reliability. We give algorithms (based on Monte-Carlo methods) to compute these Bayes estimates. Our approach allows the reliability analyst to construct a personal software reliability model simply by specifying the available prior knowledge; afterwards the results in this paper can be used to get Bayes estimates of the useful reliability parameters. Examples of possible prior physical knowledge concerning the software testing and correction environments are given. The maximum-entropy principle is used to translate this knowledge to prior distributions on the failure-rate process. Our approach is used to study some simulated and real failure data sets     

Meal simulation model of the glucose-insulin system

A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes.     

RF Power Amplifier Behavioral Modeling Based on Takenaka–Malmquist–Volterra Series

In this paper, a Takenaka–Malmquist–Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka–Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre–Volterra (LV) and Kautz–Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.     

Identification of structurally constrained second-order Volterramodels

Continuing advances in industrial control hardware and software have increased interest in the identification of nonlinear dynamic models from chemical process data. Two practically important issues are those of nonlinear model structure selection and input sequence design for adequate model identification. While the “general nonlinear model identification problem” is intractably complex, we may obtain useful insights into both of these issues by restricting our attention to special cases, building incrementally on our “linear intuition”. We consider the special case of second-order Volterra models, focusing on the effects of structural restrictions and non-Gaussian input sequences on the model identification problem. The results presented build on the work of Powers and his co-workers, who considered the unconstrained Gaussian problem, certain constrained special cases (e.g., the Hammerstein model), and identification using non-i.i.d. input sequences. Besides extending these results to a wider class of model structure constraints and input sequences, the results presented yield some useful insights into the issue of input sequence design     

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.     

Spectral modeling of switched-mode power converters

A new modeling approach for the spectral analysis of pulsewidth modulated (PWM) converters with independent inputs is developed. The key of this approach is to extend the Volterra functional series to nonlinear systems with multiple independent inputs. After formulating the state-space equations describing the dynamical behavior of PWM converters, the Volterra transfer function characterizing the output frequency response can be obtained, which is then symmetrised to form the spectral model. Since the model is developed in a closed form, it is suitable for computer analysis. The modeling approach has been applied to various PWM converters, and the results are verified. The spectral models of different power converters can readily be obtained by using this general approach     

Multisource discrimination using IIR Volterra filtering

This paper exhibits an algorithm based on Volterra-type processing in order to detect several independent sources on the same carrier frequency and to determine the number of them. The use of infinite impulse response (IIR) Volterra filtering to build a suitable discrimination test is dictated by the need of higher-order moments in this type of nonlinear problem, as well as the need of IIR for convergence     

Soft Fault Diagnosis in Analog Circuits Based on Bispectral Models

Aiming at the problem to locate soft faults in analog circuits, a new approach based on bispectral models is proposed. First, the Volterra kernels of the circuit under test (CUT) are calculated. Then, the Volterra kernels are used to construct bispectral models. By comparison with the fault features of the constructed models, soft faults of linear and weak nonlinear components in the analog circuit are identified and the faults are located. Simulations and experiments show the effectiveness of the proposed method in analog circuits.,     

Evaluation of Portal/Peripheral Route and of Algorithms for Insulin Delivery in the Closed-Loop Control of Glucose in Diabetes?A Modeling Study

In this paper we present an evaluation of portal versus peripheral routes for insulin delivery in diabetes with three representative closed-loop glucose control algorithms. A novel noninvasive approach is used which is based on a model of the blood glucose regulation system which simulates a Type I diabetic subject. The two routes and three algorithms are compared in controlling the simulated patient for 24 h, challenged with two dynamic glucose perturbations. The evaluation is performed by comparing both plasma accessible variables (e.g., glucose and insulin) and metabolic fluxes (e.g., glucose production and uptake, peripheral glucose utilization). Similar performances are achieved by the three algorithms both with peripheral and with portal infusions, especially in the postabsorptive steady state. An almost complete metabolic normalization is obtained with the portal route. With the peripheral route, normality is not restored; in particular, hyperinsulinemia and enhanced insulin-dependent glucose utilization are produced. From these simulation results, it is the site of insulin infusion, which appears to play an essential role in terms of the ability to normalize the metabolic state of a diabetic subject.     

An EM algorithm for dynamic SPECT

In this paper we present two variants of the EM algorithm for dynamic SPECT imaging. A version based on compartmental modeling which fits a sum of exponentials and a more general approach allowing for arbitrary decaying activities. The underlying probabilistic models are discussed and the incomplete and complete data spaces are shown to be physically meaningful. We indicate that the second method, leading to a convex program in the M step, is easier to treat numerically and we present a possible numerical approach. Some preliminary numerical tests indicating the feasibility of the method are included.     

Nonlinear channel modeling and identification using baseband Volterra-Parafac models

Baseband Volterra models are very useful for representing nonlinear communication channels. These models present the specificity to include only odd-order nonlinear terms, with kernels characterized by a double symmetry. The main drawback is their parametric complexity. In this paper, we develop a new class of Volterra models, called baseband Volterra-Parafac models, with a reduced parametric complexity, by using a doubly symmetric Parafac decomposition of high order Volterra kernels viewed as tensors. Three adaptive algorithms are then proposed for estimating the parameters of these models. Some Monte Carlo simulation results are presented to compare the performance of the proposed estimation algorithms, in the case of third-order baseband Volterra systems excited by PSK and QAM inputs.     

Message Delays for TDMA Scheme Under a Nonpreemptive Priority Discipline

A TDMA access-control scheme operating under a nonpreemptive message-based priority discipline is considered and analyzed. The moment generating function (MGF) of the message waiting-time is obtained, at an arbitrary station of the network, under the assumptions of a Poisson message arrival stream and random message lengths governed by a general distribution, for each priority class. Using these results, explicit formulas for any moment of the message delay can be obtained. In particular, expressions for the average and standard deviation of the message waiting-time are presented, for the case when short (e.g., interactive) messages have priority over longer (e.g., batch) messages.     

Outage Probability of MRC With Arbitrary Power Cochannel Interferers in Nakagami Fading

We propose a new approach to outage probability analysis of predetection maximal ratio combining (MRC) diversity reception in Nakagami-m fading channels. We generalize prior work in that we consider L independent cochannel interferers with arbitrary powers and fading parameters as well as the effects of additive white Gaussian noise (AWGN). Our approach results in a general expression for outage probability under very broad assumptions. Moreover, our approach leads to a closed-form expression for outage probability in most cases of interest. We also provide numerical results that demonstrate the performance improvement obtained through MRC diversity combining in the presence of cochannel interferers.     

Volterra-mapping-based behavioral modeling of nonlinear circuits and systems for high frequencies

Presents and validates a discrete-time/frequency-domain approach to the problem of Volterra-series-based behavioral modeling for high-frequency systems. The proposed technique is based on the acquisition of samples of the input/output data, both of which are sampled at the Nyquist rate corresponding to the input signal. The method is capable of identifying the time-/frequency-domain Volterra kernels/transfer functions of arbitrary causal time-invariant weakly nonlinear circuits and systems operating at high frequencies subject to essentially a general random or multitone excitation. The validity and efficiency of the proposed modeling approach has been demonstrated by several examples in high-frequency applications and good agreement has been obtained between results calculated using the proposed model and results measured or simulated with commercial simulation tools.     

Direct Reconstruction of Pharmacokinetic-Rate Images of Optical Fluorophores From NIR Measurements

In this paper, we present a new method to form pharmacokinetic-rate images of optical fluorophores directly from near infra-red (NIR) boundary measurements. We first derive a mapping from spatially resolved pharmacokinetic rates to NIR boundary measurements by combining compartmental modeling with a diffusion based NIR photon propagation model. We express this mapping as a state-space equation. Next, we introduce a spatio-temporal prior model for the pharmacokinetic-rate images and combine it with the state-space equation. We address the image formation problem using the extended Kalman filtering framework. We analyze the computational complexity of the resulting algorithms and evaluate their performance in numerical simulations. An important feature of our approach is that the reconstruction of fluorescence concentrations and compartmental modeling are combined into a single step 1) to take advantage of the inherent temporal correlations in dynamic NIR measurements, and 2) to incorporate spatio-temporal a priori information on pharmacokinetic-rate images. Simulation results show that the resulting algorithms are more robust and lead to higher signal-to-noise ratio as compared to existing approaches where the reconstruction of concentrations and compartmental modeling are treated separately. Additionally, we reconstructed pharmacokinetic-rate images using in vivo data obtained from three patients with breast tumors. The reconstruction results show that the pharmacokinetic rates of indocyanine green are higher inside the tumor region as compared to the surrounding tissue.      

Leakage-Resilient Cryptography from Minimal Assumptions

We present new constructions of leakage-resilient cryptosystems, which remain provably secure even if the attacker learns some arbitrary partial information about their internal secret-key. For any polynomial \(\ell \), we can instantiate these schemes so as to tolerate up to \(\ell \) bits of leakage. While there has been much prior work constructing such leakage-resilient cryptosystems under concrete number-theoretic and algebraic assumptions, we present the first schemes under general and minimal assumptions. In particular, we construct:

• Leakage-resilient public-key encryption from any standard public-key encryption.
• Leakage-resilient weak pseudorandom functions, symmetric-key encryption, and message-authentication codes from any one-way function.

These are the first constructions of leakage-resilient symmetric-key primitives that do not rely on public-key assumptions. We also get the first constructions of leakage-resilient public-key encryption from “search assumptions,” such as the hardness of factoring or CDH. Although our schemes can tolerate arbitrarily large amounts of leakage, the tolerated rate of leakage (defined as the ratio of leakage amount to key size) is rather poor in comparison with prior results under specific assumptions. As a building block of independent interest, we study a notion of weak hash-proof systems in the public-key and symmetric-key settings. While these inherit some of the interesting security properties of standard hash-proof systems, we can instantiate them under general assumptions.

     

Achievable information rates and the coding-spreading tradeoff in finite-sized synchronous CDMA systems

In this letter, we compute achievable information rates for finite-sized synchronous code-division multiple-access (CDMA) systems, and low-density parity-check (LDPC) codes are adopted to approach the achievable rates. The associated coding-spreading tradeoff problem is also considered using these results. We assume binary random spreading sequences, and the computed achievable rates are averaged over ensembles of spreading sequences. Unlike most prior papers, which analyze the spectral efficiency of large CDMA systems under Gaussianity assumptions (channel inputs and/or multiple-access interference), we make no such assumptions. In order to display the coding-spreading tradeoff, we plot the minimum required signal-to-noise ratio for reliable transmission as a function of information rate. It is shown that the coding-spreading tradeoff favors all coding (i.e., no spreading) when the optimal joint multiuser detector/decoder is employed, whereas for systems with a suboptimal multiuser detector and single-user decoders, there generally exists an optimal balance between coding and spreading. We also provide simulation results on the performance of LDPC-coded synchronous CDMA systems which approach the information-theoretic limits we have computed.     

Extended kalman filtering for the modeling and analysis of ICG pharmacokinetics in cancerous tumors using NIR optical methods

Compartmental modeling of indocyanine green (ICG) pharmacokinetics, as measured by near infrared (NIR) techniques, has the potential to provide diagnostic information for tumor differentiation. In this paper, we present three different compartmental models to model the pharmacokinetics of ICG in cancerous tumors. We introduce a systematic and robust approach to model and analyze ICG pharmacokinetics based on the extended Kalman filtering (EKF) framework. The proposed EKF framework effectively models multiple-compartment and multiple-measurement systems in the presence of measurement noise and uncertainties in model dynamics. It provides simultaneous estimation of pharmacokinetic parameters and ICG concentrations in each compartment. Moreover, the recursive nature of the Kalman filter estimator potentially allows real-time monitoring of time varying pharmacokinetic rates and concentration changes in different compartments. Additionally, we introduce an information theoretic criteria for the best compartmental model order selection, and residual analysis for the statistical validation of the estimates. We tested our approach using the ICG concentration data acquired from four Fischer rats carrying adenocarcinoma tumor cells. Our study indicates that, in addition to the pharmacokinetic rates, the EKF model may provide parameters that may be useful for tumor differentiation.